# t-distribution

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Related to t-distribution: T-test

## t-distribution

see t statistic.
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The standard Cauchy distribution (Student's t-distribution with one degree of freedom) has neither a moment-generating function nor finite moments of order greater than or equal to one [Johnson et al.
An important parameter in the t-distribution is the degrees of freedom.
18) The t-distribution (also known as the Student's t-distribution) is a probability distribution that is used to estimate population parameters when the sample size is small and/or when the population variance is unknown.
j] represents the offset of hidden layer node; u is the freedom degree of T-distribution function which is used to control the change of distribution form; u/(u-2)is the variance yields of T-distribution function.
Here we compare the results of the previous two sections using a specific non-Gaussian model, namely, Student's t-distribution as an example.
c] under the multivariate t-distribution assumption is different from the one under the multivariate normality assumption.
Since the effective degree of freedom is 6, from t-distribution table at 95% CL the coverage factor K = 2.
For a finite number of degrees of freedom, k is estimated as the t-factor from the Student's t-distribution based on the number of degrees of freedom and about a 95% confidence interval.
To do this, we estimate the degrees of freedom of the t-distribution, v, as part of the analysis.
For the monotonically increasing calibration curve, the variable t has a noncentral t-distribution, as described by Graybill (13) with noncentrality parameter:
The error for an estimated average is determined assuming that (X - [micro])/([sigma]/[square root]n) follows the t-distribution (where [micro] = the population average, X = the sample average, [sigma] = the sample standard deviation, n = the sample size).
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